CN113781174A - Recommendation method and system for promoting consumers to obtain favorite commodities - Google Patents
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Abstract
The invention discloses a recommendation method and a recommendation system for promoting consumers to obtain favorite commodities, wherein the method comprises the following steps: obtaining first historical consumer goods information of a first user; obtaining first favorite commodity information according to the first historical consumer commodity information; constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; acquiring multi-complementary commodity information according to the first complementary commodity topological structure; obtaining real-time consumption data of the first favorite commodity; inputting the real-time consumption data of the first favorite commodity into an intelligent matching module to obtain first output information; and generating a first recommendation scheme according to the first output information. The method solves the technical problems that the traditional commodity recommendation method in the prior art cannot accurately meet the use requirements of users, so that the recommendation performance is not perfect and the user satisfaction and experience cannot be effectively improved.
Description
Technical Field
The invention relates to the field of e-commerce correlation, in particular to a recommendation method and a recommendation system for promoting consumers to obtain favorite commodities.
Background
With the rapid development of the internet and the popularization of intelligent terminals in China, the arrival of a big data era is accelerated, the network environment is improved day by day, and the e-commerce mode is mature day by day, so that the consumption form of people gradually turns to an online shopping website from an offline entity shop, and the gradual expansion of the e-commerce market scale provides richer choices for users. However, in the face of such diverse commodity information, how a user can quickly and accurately select a commodity required by the user also becomes a topic concerned by the user and the e-commerce.
However, in the process of implementing the technical solution of the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the prior art has the technical problems that the traditional commodity recommendation method cannot meet the use requirements of users, the recommendation performance is not perfect, and the satisfaction degree and experience degree of the users cannot be effectively improved.
Disclosure of Invention
The embodiment of the application provides a recommendation method and a recommendation system for promoting consumers to obtain favorite commodities, solves the technical problems that in the prior art, a traditional commodity recommendation method cannot accurately meet the use requirements of users, the recommendation performance is not perfect enough, and the satisfaction and experience of the users cannot be effectively improved, and achieves the technical effects that complementary structure analysis is carried out on the favorite commodities of the users, intelligent matching is carried out according to the consumption complementarity of the users, and then the accuracy and intelligence of commodity recommendation are effectively improved.
In view of the foregoing problems, the embodiments of the present application provide a recommendation method and system for promoting a consumer to obtain a favorite product.
In a first aspect, an embodiment of the present application provides a recommendation method for promoting a consumer to obtain a favorite product, where the method is applied to a recommendation system for promoting a consumer to obtain a favorite product, the system includes an intelligent matching module, and the method includes: obtaining first historical consumer goods information of a first user; obtaining first favorite commodity information according to the first historical consumer commodity information; constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; acquiring multi-complementary commodity information according to the first complementary commodity topological structure; obtaining real-time consumption data of the first favorite commodity; inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and acquiring first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities; and generating a first recommendation scheme according to the first output information.
On the other hand, the application also provides a recommendation system for promoting consumers to obtain favorite commodities, which comprises: a first obtaining unit, configured to obtain first historical consumer goods information of a first user; a second obtaining unit, configured to obtain first favorite commodity information according to the first historical consumer commodity information; a first constructing unit, configured to construct a first complementary commodity topology structure with the first favorite commodity information as a central node, where the first complementary commodity topology structure is a star topology structure; a third obtaining unit, configured to obtain multi-complementary commodity information according to the first complementary commodity topological structure; a fourth obtaining unit, configured to obtain real-time consumption data of the first favorite product; the first input unit is used for inputting the real-time consumption data of the first favorite commodity into an intelligent matching module and acquiring first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multi-complementary commodity; a first generating unit configured to generate a first recommendation scheme according to the first output information.
In a third aspect, the present invention provides a recommendation system for promoting consumers to obtain favorite commodities, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of analyzing favorite commodities by obtaining historical consumption commodity information of a user, further constructing a first complementary commodity topological structure by taking the favorite commodities as a central node, obtaining multi-complementary commodity information according to the first complementary commodity topological structure, further obtaining real-time consumption data of favorite commodities of the user, inputting the real-time consumption data into an intelligent matching module according to the real-time consumption data for consumption matching, further obtaining matching consumption data of the multi-complementary commodities corresponding to the favorite commodities, and further generating a first recommendation scheme according to the matching consumption data of the multi-complementary commodities, so that the technical effects of analyzing the complementary structure of the favorite commodities of the user, and then performing intelligent matching according to the consumption complementarity of the user are achieved, and further accuracy and intelligence of commodity recommendation are effectively improved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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FIG. 1 is a schematic flow chart illustrating a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present disclosure;
FIG. 2 is a schematic view illustrating a consumption index analysis process of a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating a consumption matching comparison process of a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating a compensation matching analysis process of a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present disclosure;
FIG. 5 is a schematic view illustrating a consumption growth analysis process of a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present application;
fig. 6 is a schematic diagram illustrating a product complementation analysis process of a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present application;
FIG. 7 is a schematic diagram illustrating a color label constraint process of a recommendation method for promoting a consumer to obtain a favorite product according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram illustrating a recommendation system for promoting a consumer to obtain a favorite product according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a fourth obtaining unit 15, a first input unit 16, a first generating unit 17, a computing device 90, a memory 91, a processor 92, and an input-output interface 93.
Detailed Description
The embodiment of the application provides a recommendation method and a recommendation system for promoting consumers to obtain favorite commodities, solves the technical problems that in the prior art, a traditional commodity recommendation method cannot accurately meet the use requirements of users, the recommendation performance is not perfect enough, and the satisfaction and experience of the users cannot be effectively improved, and achieves the technical effects that complementary structure analysis is carried out on the favorite commodities of the users, intelligent matching is carried out according to the consumption complementarity of the users, and then the accuracy and intelligence of commodity recommendation are effectively improved. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
With the rapid development of the internet and the popularization of intelligent terminals in China, the arrival of a big data era is accelerated, the network environment is improved day by day, and the e-commerce mode is mature day by day, so that the consumption form of people gradually turns to an online shopping website from an offline entity shop, and the gradual expansion of the e-commerce market scale provides richer choices for users. However, in the face of such diverse commodity information, how a user can quickly and accurately select a commodity required by the user also becomes a topic concerned by the user and the e-commerce. However, the prior art has the technical problems that the traditional commodity recommendation method cannot accurately meet the use requirements of users, the recommendation performance is not perfect, and the satisfaction degree and experience degree of the users cannot be effectively improved.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a recommendation method for promoting a consumer to obtain favorite commodities, wherein the method is applied to a recommendation system for promoting the consumer to obtain the favorite commodities, the system comprises an intelligent matching module, and the method comprises the following steps: obtaining first historical consumer goods information of a first user; obtaining first favorite commodity information according to the first historical consumer commodity information; constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; acquiring multi-complementary commodity information according to the first complementary commodity topological structure; obtaining real-time consumption data of the first favorite commodity; inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and acquiring first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities; and generating a first recommendation scheme according to the first output information.
Having thus described the general principles of the present application, embodiments thereof will now be described with reference to the accompanying drawings. As can be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
Example one
As shown in fig. 1, an embodiment of the present application provides a recommendation method for promoting a consumer to obtain a favorite product, where the method is applied to a recommendation system for promoting a consumer to obtain a favorite product, the system includes an intelligent matching module, and the method includes:
step S100: obtaining first historical consumer goods information of a first user;
step S200: obtaining first favorite commodity information according to the first historical consumer commodity information;
specifically, the first historical consumed commodity information is obtained by performing data acquisition on the historical consumed information of the user, and includes information such as commodity names, commodity categories, commodity transaction amounts and commodity consumption time, and further by performing preference degree analysis on all historical consumed commodities, in the process of obtaining the preferred commodity information, the preferred commodities of the user can be obtained by dividing the categories of all commodities and calculating the consumption amount ratio of each category of commodity, meanwhile, the browsing records of the user, the adding times of a shopping cart and the like are connected, all information of the preferred commodities is further extracted, interest tags are obtained according to user data tracking, the obtained interest tags are added to the first preferred commodity information, and accurate basic data conditions are provided for later recommendation.
Step S300: constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure;
specifically, the first favorite commodity information is used as a central node, after the first favorite commodity information is analyzed according to the complementarity with the favorite commodity, a plurality of associated complementary commodities are correspondingly generated and used as child nodes to construct the first complementary commodity topological structure, furthermore, in the process of the complementarity analysis, the complementary analysis of usability compensation is carried out according to the use requirements of the commodity, corresponding requirement labels of a plurality of complementary commodities are established according to the favorite commodity of the user, for example, when one of the favorite commodities of the user is pigment, the usability of the favorite commodity is analyzed according to the pigment to obtain a plurality of connected labels, the painting pigment is used as the central node, the label is used as the child node to construct the topological structure, and a corresponding star-shaped topological structure is constructed according to the complementary labels and the central favorite commodity, wherein the constructed topological structure can abstract an entity into a star-shaped topological structure according to the size of the entity, The points are irrelevant in shape and comprise commodity digital identifiers, the lines connecting the entities are abstracted into lines containing complementary relations, and then the relations between the points and the lines are represented in a graph form, namely the relation between the favorite commodities and the complementary commodities.
Step S400: acquiring multi-complementary commodity information according to the first complementary commodity topological structure;
specifically, the first complementary commodity topological structure is constructed according to the center favorite commodity of the user and the requirement complementarity, and then the complementary commodity corresponding to each label can be obtained according to the sub-node label in the structure, for example, when one of the favorite commodities of the user is pigment, the painting pigment is used as the center node, and the complementary commodities such as a painting board, a painting brush, painting gloss oil, a positioning tape and the like are obtained one by one based on the use label analyzed by the use requirement, so that corresponding information collection is performed on all the complementary commodities according to the information category of the favorite commodity, the collected information is stored in an information analysis library, and when the corresponding information needs to be called, calling is performed according to a calling instruction, and then the information pool data of the basic information is improved, so that the commodity recommendation accuracy and reliability are correspondingly improved.
Step S500: obtaining real-time consumption data of the first favorite commodity;
specifically, the real-time consumption data of the first favorite commodity is acquired through real-time consumption data in a historical purchase record of the first user, wherein the real-time consumption data comprises real-time consumption data of favorite commodity purchase frequency, purchase time, purchase quantity, transaction amount and the like in a certain period, and corresponding calculation is completed through further storage and analysis of the real-time consumption data of the first favorite commodity, so that complementary commodity consumption analysis is further completed through the real-time consumption data, and basic data is provided for recommended commodities.
Step S600: inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and acquiring first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities;
step S700: and generating a first recommendation scheme according to the first output information.
Specifically, the real-time consumption data of the first favorite commodity is input into the intelligent matching module, wherein the matching process is to match corresponding consumption data of the complementary commodity according to the purchase quantity and the purchase index of the favorite commodity by the user, and the matching consumption data is corresponding consumption data obtained by matching according to the consumption data of the favorite commodity. For example, when the frequency and the quantity of the users purchasing the painting pigments are large, so that the users have high purchasing requirements on the painting requirements, and the use requirements of corresponding complementary commodities are increased, the intelligent matching model performs proportional analysis on the requirement relationship between favorite commodities and the corresponding complementary commodities in a certain period of history, compares the real-time consumption of the favorite commodities of the current users according to the matching proportion, generates corresponding real-time requirement matching consumption data, and then completes complex data operation through the built computer platform according to the first output information output by the intelligent matching model to generate a corresponding commodity recommendation scheme, so that the usability of the favorite commodities recommended by the system is improved, and the purposes of performing complementary structure analysis on the favorite commodities of the users and performing intelligent matching according to the consumption complementarity of the users are achieved, thereby effectively improving the accuracy and intelligence of commodity recommendation.
Further, as shown in fig. 2, in the obtaining of the first historical consumed commodity information of the first user, step S100 in this embodiment of the present application further includes:
step S110: obtaining a first consumption index by analyzing the price of the first favorite commodity;
step S120: obtaining a comprehensive consumption index by analyzing the price of the first historical consumption commodity;
step S130: determining a first recommended consumption grade according to the proportion information of the first consumption index in the comprehensive consumption index;
step S140: and generating a first constraint condition according to the first recommended consumption level, wherein the first constraint condition is used for constraining the complementary commodities.
Specifically, the consumption index of the first user to the favorite commodity is determined by analyzing the prices of all favorite commodities, further, the comprehensive consumption index of the user is obtained by analyzing the prices of historical consumed commodities, so that the expenditure proportion of the first user to the favorite commodity is determined according to the proportion between the favorite commodity and the comprehensive consumption index, and the expenditure consumption grade of the user is determined. Further, the consumption index of the favorite commodity and the market consumption data of the similar commodity are analyzed to determine the first recommended consumption grade according to the expenditure consumption grade and the market consumption grade, the consumption grade is divided into a plurality of grades, and therefore constraint conditions are generated according to the first consumption grade to constrain recommendation of complementary commodities, for example, the recommended consumption grade is in the grade C of A, B, C, D, E, and corresponding recommended complementary commodities are subjected to commodity screening in the grade C.
Further, as shown in fig. 3, before the inputting the real-time consumption data of the first favorite product into the intelligent matching module and obtaining the first output information according to the intelligent matching module, step S600 in this embodiment of the present application further includes:
step S610: obtaining real-time consumption data of the multi-complementary commodities;
step S620: the intelligent matching module carries out consumption matching according to the real-time consumption data of the multi-complementary commodities and the real-time consumption data of the first favorite commodities to obtain second output information, wherein the second output information is the real-time matching consumption data of the multi-complementary commodities;
step S630: performing data mapping comparison on the first output information and the second output information to obtain a first comparison result;
step S640: obtaining a first recommended commodity according to the first comparison result;
step S650: and generating the first recommendation scheme according to the first recommended commodity.
Specifically, the real-time consumption data of all complementary commodities are acquired, wherein the real-time consumption data of the first favorite commodity are acquired in the data acquisition process according to the real-time consumption data acquisition cycle of the first favorite commodity, the consumption of the complementary commodity and the favorite commodity are ensured to be in the same cycle, the real-time consumption data of the complementary commodity and the favorite commodity are matched through the intelligent matching module, and therefore second output information output by the intelligent matching module is obtained, wherein the first output information is the matching consumption required by a user, the second output information is the real-time consumption of the user, so that the first complementary commodity in the first output information and the corresponding first complementary commodity in the second output information are mapped and compared, and therefore a first comparison result is obtained, and when the first comparison result is not met, the complementary commodity corresponding to the first comparison result is taken as a recommended commodity, therefore, the intelligent and effective generation of the recommendation scheme is completed, and the user experience is improved.
Further, as shown in fig. 4, in the step S630 of the embodiment of the present application, the data mapping comparison is performed on the first output information and the second output information to obtain a first comparison result, and the step S includes:
step S631: generating first preset comparison data according to the matched consumption data in the first output information;
step S632: generating first real-time comparison data according to the real-time matching consumption data in the second output information;
step S633: performing compensation matching analysis on the first preset comparison data and the first real-time comparison data to obtain a first compensation commodity;
step S634: adding the first compensation commodity as a recommended commodity to the first recommendation.
Specifically, the first preset comparison data are generated according to the first output information, wherein the number of the first preset comparison data is N, the number of the first real-time comparison data is N according to the second output information, the number of the first real-time comparison data is N when the number of the first real-time comparison data is the same as that of the first preset comparison data, and the compensation matching analysis process is to extract M unsatisfied comparison results of the N comparison results and record corresponding M complementary commodity information by generating a first comparison result, a second comparison result and a third comparison result … …, so as to generate the first recommendation scheme according to the M complementary commodity information compensation. For example, when the consumption index of the drawing pigment is 78, the consumption index corresponding to the drawing board to be consumed is 11, and the consumption index corresponding to the drawing board to be purchased by the actual user is 2, so that the drawing board is required to be used as a compensation product, and so on. The technical effects that intelligent data processing is carried out based on the intelligent matching module, intelligent matching is carried out according to the user consumption complementarity, and the accuracy and the intelligence of commodity recommendation are effectively improved are achieved.
Further, as shown in fig. 5, in the obtaining of the real-time consumption data of the first favorite product, step S500 in this embodiment of the present application further includes:
step S510: constructing a first consumption curve by analyzing the real-time consumption data of the first favorite commodity;
step S520: obtaining a first demand index of the first favorite commodity by performing consumption growth analysis on the first consumption curve;
step S530: and adjusting the first recommendation scheme according to the first demand index to generate a second recommendation scheme.
Specifically, by collecting the real-time consumption data of the first favorite commodity and analyzing the number growth trend of the real-time consumption data, when the growth trend of the first user for the first favorite commodity exceeds a certain preset threshold, it indicates that the demand degree of the first user for the first favorite commodity is high, and therefore, the analysis corresponding to the first demand index needs to be completed through specific growth trend prediction and judgment of the preset threshold, and the first recommendation scheme is adjusted according to the first demand index, so as to generate a second recommendation scheme, wherein the recommendation scheme adjustment is performed by optimizing the recommendation frequency and recommendation combination of complementary commodities according to the demand index, so that the technical effect of effectively improving the accuracy and intelligence of commodity recommendation is achieved.
Further, as shown in fig. 6, where the obtaining of the multiple complementary product information according to the first complementary product topology further includes:
step S410: judging whether the commodities in the multiple complementary commodities have a complementary relationship or not;
step S420: when the commodities in the multi-complementary commodities have a complementary relationship, generating a first sub-topology structure, wherein the number of nodes of the first sub-topology structure is smaller than that of the nodes of the first complementary commodity topology structure;
step S430: when the commodities in the multi-complementary commodities do not have a complementary relationship, judging whether a first coincidence relationship exists or not;
step S440: and when the commodities in the multiple complementary commodities have a first coincidence relation, acquiring a second complementary commodity topological structure.
Further, by judging whether a complementary relationship exists in the multi-complementary commodities, namely judging whether a compensable relationship exists between the commodities complementary to the first favorite commodity, when the complementary relationship exists, a sub-topology structure can be generated, wherein the number of nodes of the sub-topology structure is smaller than that of the nodes of the first complementary commodity topology structure, so that the concentration and the main position of the first complementary commodity topology structure are guaranteed, excessive complementary commodities generated by the sub-topology structure are prevented, the concentration and the accuracy of commodity recommendation are reduced, further, if the complementary commodity topology structure does not exist, whether usability coincidence exists between all complementary commodities is judged, and when the usability coincidence exists, the complementary commodities can be further screened by adding a screening mechanism, and the purchasing power of a user is increased. For example, the added screening mechanism can screen the complementary commodities according to the celebrity preference of the user, so that the user experience is improved.
Further, the embodiment S200 of the present application further includes:
step S210: generating first screening color level data by collecting the commodity color of the first favorite commodity;
step S220: generating a first color gradation map according to the first screening color gradation data;
step S230: determining a color level preference label of the first user according to the first color level map;
step S240: and using the color-level preference label as a second constraint condition to constrain the complementary commodities.
Specifically, the colors of the first favorite commodities are collected to generate a color level card corresponding to the color of each commodity, each commodity is provided with one color level card, so that screening is performed according to the color level cards of all favorite commodities, the first screened color level data is generated according to the matching degree of the color level cards and the corresponding color level purchasing degree of the users, a color level map corresponding to the users is constructed, the color level label setting of the users is performed according to the color level map in the screening or positioning process of the complementary commodities, the detailed positioning of the complementary commodities is further completed, the purchasing power of the users is improved, further, the optimization of the color level map can be performed according to the ages and the sexes of the users, or the update of the color level map can be performed according to the commodities purchased in real time, and the interest tide color of the user browsing webpage can be used as an auxiliary reference to generate the second constraint condition for constraint, the method and the device achieve the technical effects of improving the performance of consumers for obtaining favorite commodities, and meeting the user recommendation requirements and intelligent positioning in a targeted manner.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including an application specific integrated circuit, a special CPU, a special memory, special components, and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a readable storage medium, such as a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk of a computer, and includes several instructions for causing a computer device to execute the method according to the embodiments of the present application.
To sum up, the recommendation method and system for promoting the consumer to obtain the favorite product provided by the embodiment of the application have the following technical effects:
1. the method comprises the steps of analyzing favorite commodities by obtaining historical consumption commodity information of a user, further constructing a first complementary commodity topological structure by taking the favorite commodities as a central node, obtaining multi-complementary commodity information according to the first complementary commodity topological structure, further obtaining real-time consumption data of favorite commodities of the user, inputting the real-time consumption data into an intelligent matching module according to the real-time consumption data for consumption matching, further obtaining matching consumption data of the multi-complementary commodities corresponding to the favorite commodities, and further generating a first recommendation scheme according to the matching consumption data of the multi-complementary commodities, so that the technical effects of analyzing the complementary structure of the favorite commodities of the user, and then performing intelligent matching according to the consumption complementarity of the user are achieved, and further accuracy and intelligence of commodity recommendation are effectively improved.
2. As the user color level label setting is carried out by the color level atlas, the second constraint condition is generated for constraint, the performance of obtaining the favorite commodity by the consumer is improved, and the technical effects of pertinently meeting the user recommendation requirement and intelligent positioning are achieved.
3. Due to the fact that the preset consumption demand and the real-time consumption demand are located, intelligent data processing is conducted according to the intelligent matching module, and then compensatory analysis is conducted on the complementary commodities, the accuracy and the intelligence of commodity recommendation are effectively improved.
Example two
Based on the same inventive concept as the recommendation method for promoting the consumer to obtain favorite commodities in the foregoing embodiments, the present invention further provides a recommendation system for promoting the consumer to obtain favorite commodities, as shown in fig. 8, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first historical consumer goods information of a first user;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first favorite commodity information according to the first historical consumer commodity information;
a first constructing unit 13, where the first constructing unit 13 is configured to construct a first complementary commodity topology by using the first favorite commodity information as a central node, where the first complementary commodity topology is a star topology;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain multiple complementary product information according to the first complementary product topology;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain real-time consumption data of the first favorite product;
the first input unit 16 is configured to input real-time consumption data of the first favorite product into an intelligent matching module, and obtain first output information according to the intelligent matching module, where the first output information is matching consumption data of the multiple complementary products;
a first generating unit 17, wherein the first generating unit 17 is configured to generate a first recommendation scheme according to the first output information.
Further, the system further comprises:
a fifth obtaining unit configured to obtain a first consumption index by analyzing a price of the first favorite commodity;
a sixth obtaining unit configured to obtain a comprehensive consumption index by analyzing a price of the first historical consumption item;
the first determining unit is used for determining a first recommended consumption grade according to the proportion information of the first consumption index in the comprehensive consumption index;
and the second generating unit is used for generating a first constraint condition according to the first recommended consumption level, wherein the first constraint condition is used for constraining the complementary commodities.
Further, the system further comprises:
a seventh obtaining unit, configured to obtain real-time consumption data of the multiple complementary commodities;
an eighth obtaining unit, configured to perform consumption matching by the intelligent matching module according to the real-time consumption data of the multiple complementary commodities and the real-time consumption data of the first favorite commodity, and obtain second output information, where the second output information is the real-time matching consumption data of the multiple complementary commodities;
a ninth obtaining unit, configured to perform data mapping comparison on the first output information and the second output information to obtain a first comparison result;
a tenth obtaining unit, configured to obtain a first recommended commodity according to the first comparison result;
a third generating unit configured to generate the first recommended scheme according to the first recommended item.
Further, the system further comprises:
a fourth generating unit, configured to generate first preset comparison data according to the matching consumption data in the first output information;
a fifth generating unit, configured to generate first real-time comparison data according to the real-time matching consumption data in the second output information;
an eleventh obtaining unit, configured to perform compensation matching analysis on the first preset comparison data and the first real-time comparison data to obtain a first compensated commodity;
a first adding unit for adding the first compensation commodity as a recommended commodity to the first recommendation scheme.
Further, the system further comprises:
the second construction unit is used for constructing a first consumption curve by analyzing the real-time consumption data of the first favorite commodity;
a twelfth obtaining unit, configured to obtain a first demand index of the first favorite product by performing consumption growth analysis on the first consumption curve;
a sixth generating unit, configured to adjust the first recommendation scheme according to the first demand index, and generate a second recommendation scheme.
Further, the system further comprises:
the first judging unit is used for judging whether the commodities in the multiple complementary commodities have a complementary relationship or not;
a seventh generating unit, configured to generate a first sub-topology structure when goods in the multiple complementary goods have a complementary relationship, where the number of nodes of the first sub-topology structure is smaller than the number of nodes of the first complementary goods topology structure;
a second judging unit configured to judge whether a first coincidence relation exists when a complementary relation does not exist among the multiple complementary commodities;
a thirteenth obtaining unit, configured to obtain a second complementary product topology when the products in the multiple complementary products have the first coincidence relation.
Further, the system further comprises:
an eighth generating unit, configured to generate first screening color rank data by collecting the product colors of the first favorite product;
a ninth generating unit, configured to generate a first color gradation map according to the first screened color gradation data;
a second determining unit, configured to determine, according to the first color gamut map, a color gamut preference label of the first user;
a first operation unit configured to constrain the complementary product using the color-rank preference label as a second constraint condition.
In the embodiment of the present application, the network device and the terminal device may be divided into functional modules according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one receiving module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation. Through the foregoing detailed description of the recommendation method for promoting consumers to obtain favorite products, those skilled in the art can clearly know the implementation method of the recommendation system for promoting consumers to obtain favorite products in the present embodiment, and therefore, for the brevity of the description, detailed description is omitted here.
Exemplary electronic device
FIG. 9 is a schematic diagram of a computing device of the present application. The computing device 90 shown in fig. 9 may include a memory 91, a processor 92, and an input/output interface 93. The memory 91, the processor 92 and the input/output interface 93 are connected through an internal connection path, the memory 33 is used for storing instructions, and the processor 92 is used for executing the instructions stored in the memory 91 so as to control the input/output interface 93 to receive input data and information and output data such as operation results.
FIG. 9 is a schematic diagram of a computing device of another embodiment of the present application. The computing device 90 shown in fig. 9 may include a memory 91, a processor 92, and an input/output interface 93. Wherein, the memory 91, the processor 92 and the input/output interface 93 are connected through an internal connection path, the memory 91 is used for storing instructions, and the processor 92 is used for executing the instructions stored in the memory 92, so as to control the input/output interface 93 to receive input data and information and output data such as operation results.
In implementation, the steps of the above method may be performed by instructions in the form of hardware, integrated logic circuits, or software in the processor 92. The method for recognizing the abnormal message and/or the method for training the abnormal message recognition model disclosed by the embodiment of the application can be directly implemented by a hardware processor, or implemented by combining hardware and software modules in the processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 91, and the processor 92 reads the information in the memory 91 and performs the steps of the above method in combination with the hardware thereof. To avoid repetition, it is not described in detail here.
It should be understood that in the embodiments of the present application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be appreciated that in embodiments of the present application, the memory may comprise both read-only memory and random access memory, and may provide instructions and data to the processor. A portion of the processor may also include non-volatile random access memory. For example, the processor may also store information of the device type.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be read by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., hard Disk, magnetic tape), an optical medium (e.g., Digital Versatile Disk (DVD)), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A recommendation method for promoting consumers to obtain favorite commodities is applied to a recommendation system for promoting consumers to obtain favorite commodities, the system comprises an intelligent matching module, and the method comprises the following steps:
obtaining first historical consumer goods information of a first user;
obtaining first favorite commodity information according to the first historical consumer commodity information;
constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure;
acquiring multi-complementary commodity information according to the first complementary commodity topological structure;
obtaining real-time consumption data of the first favorite commodity;
inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and acquiring first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities;
and generating a first recommendation scheme according to the first output information.
2. The method of claim 1, wherein the obtaining first historical consumer good information for the first user, the method further comprises:
obtaining a first consumption index by analyzing the price of the first favorite commodity;
obtaining a comprehensive consumption index by analyzing the price of the first historical consumption commodity;
determining a first recommended consumption grade according to the proportion information of the first consumption index in the comprehensive consumption index;
and generating a first constraint condition according to the first recommended consumption level, wherein the first constraint condition is used for constraining the complementary commodities.
3. The method of claim 1, wherein said inputting real-time consumption data of said first favorite product into said intelligent matching module, before obtaining first output information according to said intelligent matching module, further comprises:
obtaining real-time consumption data of the multi-complementary commodities;
the intelligent matching module carries out consumption matching according to the real-time consumption data of the multi-complementary commodities and the real-time consumption data of the first favorite commodities to obtain second output information, wherein the second output information is the real-time matching consumption data of the multi-complementary commodities;
performing data mapping comparison on the first output information and the second output information to obtain a first comparison result;
obtaining a first recommended commodity according to the first comparison result;
and generating the first recommendation scheme according to the first recommended commodity.
4. The method of claim 3, wherein the data mapping comparison between the first output information and the second output information obtains a first comparison result, and the method further comprises:
generating first preset comparison data according to the matched consumption data in the first output information;
generating first real-time comparison data according to the real-time matching consumption data in the second output information;
performing compensation matching analysis on the first preset comparison data and the first real-time comparison data to obtain a first compensation commodity;
adding the first compensation commodity as a recommended commodity to the first recommendation.
5. The method of claim 1, wherein the obtaining real-time consumption data for the first preferred good, the method further comprises:
constructing a first consumption curve by analyzing the real-time consumption data of the first favorite commodity;
obtaining a first demand index of the first favorite commodity by performing consumption growth analysis on the first consumption curve;
and adjusting the first recommendation scheme according to the first demand index to generate a second recommendation scheme.
6. The method of claim 1, wherein the obtaining multiple complementary merchandise information is based on the first complementary merchandise topology, the method further comprising:
judging whether the commodities in the multiple complementary commodities have a complementary relationship or not;
when the commodities in the multi-complementary commodities have a complementary relationship, generating a first sub-topology structure, wherein the number of nodes of the first sub-topology structure is smaller than that of the nodes of the first complementary commodity topology structure;
when the commodities in the multi-complementary commodities do not have a complementary relationship, judging whether a first coincidence relationship exists or not;
and when the commodities in the multiple complementary commodities have a first coincidence relation, acquiring a second complementary commodity topological structure.
7. The method of claim 1, wherein the method further comprises:
generating first screening color level data by collecting the commodity color of the first favorite commodity;
generating a first color gradation map according to the first screening color gradation data;
determining a color level preference label of the first user according to the first color level map;
and using the color-level preference label as a second constraint condition to constrain the complementary commodities.
8. A recommendation system for promoting consumer acquisition of a favorite product, wherein the system comprises:
a first obtaining unit, configured to obtain first historical consumer goods information of a first user;
a second obtaining unit, configured to obtain first favorite commodity information according to the first historical consumer commodity information;
a first constructing unit, configured to construct a first complementary commodity topology structure with the first favorite commodity information as a central node, where the first complementary commodity topology structure is a star topology structure;
a third obtaining unit, configured to obtain multi-complementary commodity information according to the first complementary commodity topological structure;
a fourth obtaining unit, configured to obtain real-time consumption data of the first favorite product;
the first input unit is used for inputting the real-time consumption data of the first favorite commodity into an intelligent matching module and acquiring first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multi-complementary commodity;
a first generating unit configured to generate a first recommendation scheme according to the first output information.
9. A recommendation system for promoting consumer acquisition of a preferred item, comprising at least one processor and a memory, the at least one processor coupled to the memory for reading and executing instructions in the memory to perform the method of any of claims 1-7.
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